Whole page personalization with cyclic dependencies

ABSTRACT

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, cause the one or more processors to perform functions including: reconfiguring a webpage as an undirected graph; identifying a cyclic dependency in the undirected graph; iterating processing one or more inferences over the cyclic dependency for each pair of the nodes of the set of the nodes; breaking one or more of edges of the undirected graph; determining, based at least in part on compatibility functions of the one or more edges remaining after breaking the one or more of the edges, a probability of the webpage having exceeded a predetermined threshold to cause a user to take an action; and sending instructions to display the webpage. Other embodiments are described.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/073,202, which is a continuation-in-part of U.S. application Ser. No.16/525,298 filed Jul. 29, 2019, now U.S. Pat. No. 11,194,875, which is acontinuation of U.S. application Ser. No. 15/420,757, filed Jan. 31,2017, now U.S. Pat. No. 10,366,133. U.S. application Ser. Nos.17/073,202, 16/525,298 and 15/420,757 and U.S. Pat. Nos. 10,366,133 and11,194,875 are incorporated herein by reference in their entirety.

TECHNICAL FIELD

This disclosure relates generally to systems for whole pagepersonalization with cyclic dependencies, and related methods.

BACKGROUND

System bandwidth can become slow or bottlenecked when retrieving searchresults for a search query. Many times, when a user of a web site, suchas an eCommerce website, has difficulty finding an item, the user canconduct numerous user actions and/or item activities (e.g., clicking onone or more items or entering new search terms). These user actionsand/or item activities can decrease the efficiency of a system byincreasing the amount of item information retrieved from a database. Theability to personalize a webpage and/or website experience can decreasethe demand on system resources and improve user experience. Accordingly,there is a need for systems and methods to provide for whole pagepersonalization.

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided in which:

FIG. 1 illustrates a front elevation view of a computer system that issuitable for implementing at least part of a central computer system;

FIG. 2 illustrates a representative block diagram of exemplary elementsincluded on the circuit boards inside a chassis of the computer systemof FIG. 1 ;

FIG. 3 illustrates a representative block diagram of a system, accordingto an embodiment;

FIG. 4 illustrates a representative block diagram of a portion of thesystem of FIG. 3 , according to an embodiment;

FIG. 5 illustrates is a flowchart for a method, according to anembodiment;

FIG. 6 illustrates an exemplary webpage as a random field, according toan embodiment;

FIGS. 7A-7B illustrates a flowchart for a method, according to anotherembodiment;

FIG. 8 illustrates an exemplary webpage as a random field withnon-cyclic dependencies;

FIG. 9 illustrates an exemplary webpage as a random field with cyclicdependencies; and

FIG. 10 illustrates an exemplary webpage as a random field with cyclicdependencies.

For simplicity and clarity of illustration, the drawing figuresillustrate the general manner of construction, and descriptions anddetails of well-known features and techniques may be omitted to avoidunnecessarily obscuring the present disclosure. Additionally, elementsin the drawing figures are not necessarily drawn to scale. For example,the dimensions of some of the elements in the figures may be exaggeratedrelative to other elements to help improve understanding of embodimentsof the present disclosure. The same reference numerals in differentfigures denote the same elements.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.Furthermore, the terms “include,” and “have,” and any variationsthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, system, article, device, or apparatus that comprises alist of elements is not necessarily limited to those elements, but mayinclude other elements not expressly listed or inherent to such process,method, system, article, device, or apparatus.

The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,”“under,” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is to be understood that the terms soused are interchangeable under appropriate circumstances such that theembodiments of the apparatus, methods, and/or articles of manufacturedescribed herein are, for example, capable of operation in otherorientations than those illustrated or otherwise described herein.

The terms “couple,” “coupled,” “couples,” “coupling,” and the likeshould be broadly understood and refer to connecting two or moreelements mechanically and/or otherwise. Two or more electrical elementsmay be electrically coupled together, but not be mechanically orotherwise coupled together. Coupling may be for any length of time,e.g., permanent or semi-permanent or only for an instant. “Electricalcoupling” and the like should be broadly understood and includeelectrical coupling of all types. The absence of the word “removably,”“removable,” and the like near the word “coupled,” and the like does notmean that the coupling, etc. in question is or is not removable.

As defined herein, “approximately” can, in some embodiments, mean withinplus or minus ten percent of the stated value. In other embodiments,“approximately” can mean within plus or minus five percent of the statedvalue. In further embodiments, “approximately” can mean within plus orminus three percent of the stated value. In yet other embodiments,“approximately” can mean within plus or minus one percent of the statedvalue.

DESCRIPTION OF EXAMPLES OF EMBODIMENTS

Some embodiments include a system. In many embodiments, the system cancomprise one or more processing modules and one or more non-transitorystorage modules storing computing instructions configured to run on theone or more processing modules and perform acts. In many embodiments,the acts can comprise modeling a webpage as a random field, wherein therandom field comprises an undirected graph. In some embodiments, theundirected graph can comprise one or more nodes and one or more edges,wherein each edge of the one or more edges is between two differentnodes of the one or more nodes, each node of the one or more nodescomprises one or more placements on the webpage and a goodness functionof one or more goodness functions associated with one or more webpagemodules, and each edge of the or one more edges comprises acompatibility function based at least in part on the one or moregoodness functions of the two different nodes of the one or more nodesassociated with each edge of the one or more edges. In many embodiments,the acts further can comprise determining a probability of the webpagehaving exceeded a predetermined threshold based at least in part on theone or more compatibility functions by determining a first placement ofthe one or more placements of a first webpage module of the one or morewebpage modules and determining a second placement of the one or moreplacements of a second webpage module of the one or more webpagemodules. In various embodiments, the one or more webpage modules cancomprise an advertisement, a search recommendation, or an itemrecommendation. In some embodiments, the acts further can comprisefacilitating a display of the webpage based at least in part on theprobability of the webpage.

Many embodiments can comprise a method. In some embodiments, the methodcan comprise modeling a webpage as a random field, wherein the randomfield comprises an undirected graph. In some embodiments, the undirectedgraph can comprise one or more nodes and one or more edges, wherein eachedge of the one or more edges is between two different nodes of the oneor more nodes, each node of the one or more nodes comprises one or moreplacements on the webpage and a goodness function of one or moregoodness functions associated with one or more webpage modules, and eachedge of the or one more edges comprises a compatibility function basedat least in part on the one or more goodness functions of the twodifferent nodes of the one or more nodes associated with each edge ofthe one or more edges. In many embodiments, the acts further cancomprise determining a probability of the webpage having exceeded apredetermined threshold based at least in part on the one or morecompatibility functions by determining a first placement of the one ormore placements of a first webpage module of the one or more webpagemodules and determining a second placement of the one or more placementsof a second webpage module of the one or more webpage modules. Invarious embodiments, the one or more webpage modules can comprise anadvertisement, a search recommendation, or an item recommendation. Insome embodiments, the acts further can comprise facilitating a displayof the webpage based at least in part on the probability of the webpage.

A number of embodiments comprise a method. In some embodiments, themethod can comprise receiving a search query from a user and modeling awebpage as a random field. In many embodiments, the random field cancomprise an undirected graph. In some embodiments, the undirected graphcan comprise one or more nodes and one or more edges, wherein each edgeof the one or more edges is between two different nodes of the one ormore nodes, each node of the one or more nodes can comprise one or moreplacements on the webpage and a goodness function of one or moregoodness functions associated with one or more webpage modules, and eachedge of the or one more edges comprises a compatibility function basedat least in part on the one or more goodness functions of the twodifferent nodes of the one or more nodes associated with each edge ofthe one or more edges. In many embodiments, the method further cancomprise determining a probability of the webpage having exceeded apredetermined threshold based at least in part on the one or morecompatibility functions by determining a first placement of the one ormore placements of a first webpage module of the one or more webpagemodules and determining a second placement of the one or more placementsof a second webpage module of the one or more webpage modules. In someembodiments, the one or more webpage modules can comprise anadvertisement associated with the search query a search recommendationwith the search query, or an item recommendation with the search query.In various embodiments, the method further can comprise facilitating adisplay of the webpage based at least in part on the probability of thewebpage.

A number of embodiments include a system. The system can include one ormore processors and one or more non-transitory computer-readable mediastoring computing instructions configured to run on the one or moreprocessors and perform certain acts. The acts can include modeling awebpage as a random field. The random field can include an undirectedgraph including two or more nodes and one or more edges. Each node ofthe two or more nodes can include one or more placements on the webpageand can include a goodness function of one or more goodness functionsassociated with one or more webpage elements. Each edge of the one ormore edges can include a compatibility function based at least in parton one or more goodness functions of two different nodes of the two ormore nodes associated with each edge of the one or more edges. The actsalso can include determining a probability of the webpage havingexceeded a predetermined threshold based at least in part on one or moreof the compatibility functions of the one or more edges. The actsfurther can include sending instructions to display the webpage based atleast in part on the probability of the webpage having exceeded thepredetermined threshold.

Various embodiments include a method. The method can include modeling awebpage as a random field. The random field can include an undirectedgraph including two or more nodes and one or more edges. Each node ofthe two or more nodes can include one or more placements on the webpageand can include a goodness function of one or more goodness functionsassociated with one or more webpage elements. Each edge of the one ormore edges can include a compatibility function based at least in parton the one or more goodness functions of two different nodes of the twoor more nodes associated with each edge of the one or more edges. Themethod also can include determining a probability of the webpage havingexceeded a predetermined threshold based at least in part on one or moreof the compatibility functions of the one or more edges. Additionally,the method further can include sending instructions to display thewebpage based at least in part on the probability of the webpage havingexceeded the predetermined threshold.

A number of embodiments can include a system including one or more oneor more processors and one or more non-transitory computer-readablemedia storing computing instructions configured to run on the one ormore processors and perform certain acts. The acts can include modelinga webpage as a random field. The random field can include an undirectedgraph including nodes and edges. The acts also can include identifying acyclic dependency in the undirected graph. The cyclic dependency caninvolve at least three of the nodes. The acts additionally can includebreaking one or more of the edges of the undirected graph that connectsthe at least three of the nodes in the cyclic dependency. The acts alsocan include determining a probability of the webpage having exceeded apredetermined threshold based on compatibility functions of the edges,as updated. The acts also can include ending instructions to display thewebpage based at least in part on the probability of the webpage havingexceeded the predetermined threshold.

Various embodiments can include a method being implemented via executionof computing instructions configured to run on one or more processorsand stored at one or more non-transitory computer-readable media. Themethod can include modeling a webpage as a random field. The randomfield can include an undirected graph including nodes and edges. Themethod also an include identifying a cyclic dependency in the undirectedgraph. The cyclic dependency can involve at least three of the nodes.The method further can include breaking one or more of the edges of theundirected graph that connects the at least three of the nodes in thecyclic dependency. The method additionally can include determining aprobability of the webpage having exceeded a predetermined thresholdbased on compatibility functions of the edges, as updated. The methodalso can include sending instructions to display the webpage based atleast in part on the probability of the webpage having exceeded thepredetermined threshold.

A number of embodiments include a system. The system including one ormore processors and one or more non-transitory computer-readable mediastoring computing instructions that, when run on the one or moreprocessors, cause the one or more processors to perform certain acts.The acts can include reconfiguring a webpage as an undirected graph. Theacts also can include identifying a cyclic dependency in the undirectedgraph. The cyclic dependency can include a set of nodes of theundirected graph. The acts further can include iterating processing oneor more inferences over the cyclic dependency for each pair of the nodesof the set of the nodes. The acts additionally can include breaking oneor more of edges of the undirected graph. The one or more edges coupletogether the set of the nodes. The acts further can include determining,based at least in part on compatibility functions of the one or moreedges remaining after breaking the one or more of the edges, aprobability of the webpage having exceeded a predetermined threshold tocause a user to take an action. The acts also can include sendinginstructions to display the webpage.

Various embodiments include a method. The method being implemented viaexecution of computing instructions configured to run on one or moreprocessors and stored on one or more non-transitory computer-readablemedia. The method can include reconfiguring a webpage as an undirectedgraph. The method also can include identifying a cyclic dependency inthe undirected graph. The cyclic dependency can include a set of nodesof the undirected graph. The method further can include iteratingprocessing one or more inferences over the cyclic dependency for eachpair of the nodes of the set of the nodes. The method additionally caninclude breaking one or more of edges of the undirected graph. The oneor more edges couple together the set of the nodes. The method furthercan include determining, based at least in part on compatibilityfunctions of the one or more edges remaining after breaking the one ormore of the edges, a probability of the webpage having exceeded apredetermined threshold to cause a user to take an action. The methodalso can include sending instructions to display the webpage.

Turning to the drawings, FIG. 1 illustrates an exemplary embodiment of acomputer system 100, all of which or a portion of which can be suitablefor (i) implementing part or all of one or more embodiments of thetechniques, methods, and systems and/or (ii) implementing and/oroperating part or all of one or more embodiments of the memory storagemodules described herein. As an example, a different or separate one ofa chassis 102 (and its internal components) can be suitable forimplementing part or all of one or more embodiments of the techniques,methods, and/or systems described herein. Furthermore, one or moreelements of computer system 100 (e.g., a monitor 106, a keyboard 104,and/or a mouse 110, etc.) also can be appropriate for implementing partor all of one or more embodiments of the techniques, methods, and/orsystems described herein. Computer system 100 can comprise chassis 102containing one or more circuit boards (not shown), a Universal SerialBus (USB) port 112, a Compact Disc Read-Only Memory (CD-ROM) and/orDigital Video Disc (DVD) drive 116, and a hard drive 114. Arepresentative block diagram of the elements included on the circuitboards inside chassis 102 is shown in FIG. 2 . A central processing unit(CPU) 210 in FIG. 2 is coupled to a system bus 214 in FIG. 2 . Invarious embodiments, the architecture of CPU 210 can be compliant withany of a variety of commercially distributed architecture families.

Continuing with FIG. 2 , system bus 214 also is coupled to a memorystorage unit 208, where memory storage unit 208 can comprise (i)non-volatile (e.g., non-transitory) memory, such as, for example, readonly memory (ROM) and/or (ii) volatile (e.g., transitory) memory, suchas, for example, random access memory (RAM). The non-volatile memory canbe removable and/or non-removable non-volatile memory. Meanwhile, RAMcan include dynamic RAM (DRAM), static RAM (SRAM), etc. Further, ROM caninclude mask-programmed ROM, programmable ROM (PROM), one-timeprogrammable ROM (OTP), erasable programmable read-only memory (EPROM),electrically erasable programmable ROM (EEPROM) (e.g., electricallyalterable ROM (EAROM) and/or flash memory), etc. The memory storagemodule(s) of the various embodiments disclosed herein can comprisememory storage unit 208, an external memory storage drive (not shown),such as, for example, a USB-equipped electronic memory storage drivecoupled to universal serial bus (USB) port 112 (FIGS. 1-2 ), hard drive114 (FIGS. 1-2 ), a CD-ROM and/or DVD for use with a CD-ROM and/or DVDdrive 116 (FIGS. 1-2 ), floppy disk for use with a floppy disk drive(not shown), an optical disc (not shown), a magneto-optical disc (nowshown), magnetic tape (not shown), etc. Further, non-volatile ornon-transitory memory storage module(s) refer to the portions of thememory storage module(s) that are non-volatile (e.g., non-transitory)memory.

In various examples, portions of the memory storage module(s) of thevarious embodiments disclosed herein (e.g., portions of the non-volatilememory storage module(s)) can be encoded with a boot code sequencesuitable for restoring computer system 100 (FIG. 1 ) to a functionalstate after a system reset. In addition, portions of the memory storagemodule(s) of the various embodiments disclosed herein (e.g., portions ofthe non-volatile memory storage module(s)) can comprise microcode suchas a Basic Input-Output System (BIOS) operable with computer system 100(FIG. 1 ). In the same or different examples, portions of the memorystorage module(s) of the various embodiments disclosed herein (e.g.,portions of the non-volatile memory storage module(s)) can comprise anoperating system, which can be a software program that manages thehardware and software resources of a computer and/or a computer network.The BIOS can initialize and test components of computer system 100 (FIG.1 ) and load the operating system. Meanwhile, the operating system canperform basic tasks such as, for example, controlling and allocatingmemory, prioritizing the processing of instructions, controlling inputand output devices, facilitating networking, and managing files.Exemplary operating systems can comprise one of the following: (i)Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond,Wash., United States of America, (ii) Mac® OS X by Apple Inc. ofCupertino, Calif., United States of America, (iii) UNIX® OS, and (iv)Linux® OS. Further exemplary operating systems can comprise one of thefollowing: (i) the iOS® operating system by Apple Inc. of Cupertino,Calif., United States of America, (ii) the Blackberry® operating systemby Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) theWebOS operating system by LG Electronics of Seoul, South Korea, (iv) theAndroid™ operating system developed by Google, of Mountain View, Calif.,United States of America, (v) the Windows Mobile™ operating system byMicrosoft Corp. of Redmond, Wash., United States of America, or (vi) theSymbian™ operating system by Accenture PLC of Dublin, Ireland.

As used herein, “processor” and/or “processing module” means any type ofcomputational circuit, such as but not limited to a microprocessor, amicrocontroller, a controller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, agraphics processor, a digital signal processor, or any other type ofprocessor or processing circuit capable of performing the desiredfunctions. In some examples, the one or more processing modules of thevarious embodiments disclosed herein can comprise CPU 210.

In the depicted embodiment of FIG. 2 , various I/O devices such as adisk controller 204, a graphics adapter 224, a video controller 202, akeyboard adapter 226, a mouse adapter 206, a network adapter 220, andother I/O devices 222 can be coupled to system bus 214. Keyboard adapter226 and mouse adapter 206 are coupled to keyboard 104 (FIGS. 1-2 ) andmouse 110 (FIGS. 1-2 ), respectively, of computer system 100 (FIG. 1 ).While graphics adapter 224 and video controller 202 are indicated asdistinct units in FIG. 2 , video controller 202 can be integrated intographics adapter 224, or vice versa in other embodiments. Videocontroller 202 is suitable for monitor 106 (FIGS. 1-2 ) to displayimages on a screen 108 (FIG. 1 ) of computer system 100 (FIG. 1 ). Diskcontroller 204 can control hard drive 114 (FIGS. 1-2 ), USB port 112(FIGS. 1-2 ), and CD-ROM drive 116 (FIGS. 1-2 ). In other embodiments,distinct units can be used to control each of these devices separately.

Network adapter 220 can be suitable to connect computer system 100 (FIG.1 ) to a computer network by wired communication (e.g., a wired networkadapter) and/or wireless communication (e.g., a wireless networkadapter). In some embodiments, network adapter 220 can be plugged orcoupled to an expansion port (not shown) in computer system 100 (FIG. 1). In other embodiments, network adapter 220 can be built into computersystem 100 (FIG. 1 ). For example, network adapter 220 can be built intocomputer system 100 (FIG. 1 ) by being integrated into the motherboardchipset (not shown), or implemented via one or more dedicatedcommunication chips (not shown), connected through a PCI (peripheralcomponent interconnector) or a PCI express bus of computer system 100(FIG. 1 ) or USB port 112 (FIG. 1 ).

Returning now to FIG. 1 , although many other components of computersystem 100 are not shown, such components and their interconnection arewell known to those of ordinary skill in the art. Accordingly, furtherdetails concerning the construction and composition of computer system100 and the circuit boards inside chassis 102 are not discussed herein.

Meanwhile, when computer system 100 is running, program instructions(e.g., computer instructions) stored on one or more of the memorystorage module(s) of the various embodiments disclosed herein can beexecuted by CPU 210 (FIG. 2 ). At least a portion of the programinstructions, stored on these devices, can be suitable for carrying outat least part of the techniques and methods described herein.

Further, although computer system 100 is illustrated as a desktopcomputer in FIG. 1 , there can be examples where computer system 100 maytake a different form factor while still having functional elementssimilar to those described for computer system 100. In some embodiments,computer system 100 may comprise a single computer, a single server, ora cluster or collection of computers or servers, or a cloud of computersor servers. Typically, a cluster or collection of servers can be usedwhen the demand on computer system 100 exceeds the reasonable capabilityof a single server or computer. In certain embodiments, computer system100 may comprise a portable computer, such as a laptop computer. Incertain other embodiments, computer system 100 may comprise a mobileelectronic device, such as a smartphone. In certain additionalembodiments, computer system 100 may comprise an embedded system.

Skipping ahead now in the drawings, FIG. 3 illustrates a representativeblock diagram of a system 300, according to an embodiment. System 300 ismerely exemplary and embodiments of the system are not limited to theembodiments presented herein. System 300 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, certain elements or modules of system 300can perform various methods and/or activities of those methods. In theseor other embodiments, the methods and/or the activities of the methodscan be performed by other suitable elements or modules of system 300.

Generally, therefore, system 300 can be implemented with hardware and/orsoftware, as described herein. In some embodiments, part or all of thehardware and/or software can be conventional, while in these or otherembodiments, part or all of the hardware and/or software can becustomized (e.g., optimized) for implementing part or all of thefunctionality of system 300 described herein.

In a number of embodiments, system 300 can comprise a search system 310,a personalization system 320, and a display system 360. In someembodiments, search system 310, personalization system 320, and displaysystem 360 can each be a computer system 100 (FIG. 1 ), as describedabove, and can each be a single computer, a single server, or a clusteror collection of computers or servers. In some embodiments, searchsystem 310 and/or personalization system 320 can be in communicationwith an inventory database (not shown) which can track distinct items(e.g., stock keeping units (SKUs)), and images of the distinct items, ina product catalog, which can be ordered through the online retailer andwhich can be housed at one or more warehouses. In many embodiments,warehouses can comprise brick-and-mortar stores, distribution centers,and/or other storage facilities.

In many embodiments, search system 310, personalization system 320,and/or display system 360 can each comprise one or more input devices(e.g., one or more keyboards, one or more keypads, one or more pointingdevices such as a computer mouse or computer mice, one or moretouchscreen displays, a microphone, etc.), and/or can each comprise oneor more display devices (e.g., one or more monitors, one or more touchscreen displays, projectors, etc.). In these or other embodiments, oneor more of the input device(s) can be similar or identical to keyboard104 (FIG. 1 ) and/or a mouse 110 (FIG. 1 ). Further, one or more of thedisplay device(s) can be similar or identical to monitor 106 (FIG. 1 )and/or screen 108 (FIG. 1 ). The input device(s) and the displaydevice(s) can be coupled to the processing module(s) and/or the memorystorage module(s) of search system 310, personalization system 320,and/or display system 360 in a wired manner and/or a wireless manner,and the coupling can be direct and/or indirect, as well as locallyand/or remotely. As an example of an indirect manner (which may or maynot also be a remote manner), a keyboard-video-mouse (KVM) switch can beused to couple the input device(s) and the display device(s) to theprocessing module(s) and/or the memory storage module(s). In someembodiments, the KVM switch also can be part of search system 310,personalization system 320, and/or display system 360. In a similarmanner, the processing module(s) and the memory storage module(s) can belocal and/or remote to each other.

In many embodiments, search system 310 and/or display system 360 can beconfigured to communicate with one or more user computers 340 and 341.In some embodiments, user computers 340 and 341 also can be referred toas customer computers. In some embodiments, search system 310 and/ordisplay system 360 can communicate or interface (e.g. interact) with oneor more customer computers (such as user computers 340 and 341) througha network 330. In some embodiments, network 330 can be an internet, anintranet that is not open to the public, an email system, and/or atexting system. In many embodiments, network 330 can comprise one ormore electronic transmission channels. In many embodiments, theelectronic transmission channels can comprise an email, a text message,and/or an electronic notice or message. Accordingly, in manyembodiments, search system 310 and/or display system 360 (and/or thesoftware used by such systems) can refer to a back end of system 300operated by an operator and/or administrator of system 300, and usercomputers 340 and 341 (and/or the software used by such systems) canrefer to a front end of system 300 used by one or more users 350 and351, respectively. In some embodiments, users 350 and 351 also can bereferred to as customers, in which case, user computers 340 and 341 canbe referred to as customer computers. In these or other embodiments, theoperator and/or administrator of system 300 can manage system 300, theprocessing module(s) of system 300, and/or the memory storage module(s)of system 300 using the input device(s) and/or display device(s) ofsystem 300.

Meanwhile, in many embodiments, search system 310, personalizationsystem 320, and/or display system 360 also can be configured tocommunicate with one or more databases. The one or more database cancomprise a product database that contains information about products,items, or SKUs sold by a retailer. The one or more databases can bestored on one or more memory storage modules (e.g., non-transitorymemory storage module(s)), which can be similar or identical to the oneor more memory storage module(s) (e.g., non-transitory memory storagemodule(s)) described above with respect to computer system 100 (FIG. 1). Also, in some embodiments, for any particular database of the one ormore databases, that particular database can be stored on a singlememory storage module of the memory storage module(s), and/or thenon-transitory memory storage module(s) storing the one or moredatabases or the contents of that particular database can be spreadacross multiple ones of the memory storage module(s) and/ornon-transitory memory storage module(s) storing the one or moredatabases, depending on the size of the particular database and/or thestorage capacity of the memory storage module(s) and/or non-transitorymemory storage module(s).

The one or more databases can each comprise a structured (e.g., indexed)collection of data and can be managed by any suitable databasemanagement systems configured to define, create, query, organize,update, and manage database(s). Exemplary database management systemscan include MySQL (Structured Query Language) Database, PostgreSQLDatabase, Microsoft SQL Server Database, Oracle Database, SAP (Systems,Applications, & Products) Database, and IBM DB2 Database.

Meanwhile, communication between search system 310, personalizationsystem 320, display system 360, and/or the one or more databases can beimplemented using any suitable manner of wired and/or wirelesscommunication. Accordingly, system 300 can comprise any software and/orhardware components configured to implement the wired and/or wirelesscommunication. Further, the wired and/or wireless communication can beimplemented using any one or any combination of wired and/or wirelesscommunication network topologies (e.g., ring, line, tree, bus, mesh,star, daisy chain, hybrid, etc.) and/or protocols (e.g., personal areanetwork (PAN) protocol(s), local area network (LAN) protocol(s), widearea network (WAN) protocol(s), cellular network protocol(s), powerlinenetwork protocol(s), etc.). Exemplary PAN protocol(s) can compriseBluetooth, Zigbee, Wireless Universal Serial Bus (USB), Z-Wave, etc.;exemplary LAN and/or WAN protocol(s) can comprise Institute ofElectrical and Electronic Engineers (IEEE) 802.3 (also known asEthernet), IEEE 802.11 (also known as WiFi), etc.; and exemplarywireless cellular network protocol(s) can comprise Global System forMobile Communications (GSM), General Packet Radio Service (GPRS), CodeDivision Multiple Access (CDMA), Evolution-Data Optimized (EV-DO),Enhanced Data Rates for GSM Evolution (EDGE), Universal MobileTelecommunications System (UMTS), Digital Enhanced CordlessTelecommunications (DECT), Digital AMPS (IS-136/Time Division MultipleAccess (TDMA)), Integrated Digital Enhanced Network (iDEN), EvolvedHigh-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc.The specific communication software and/or hardware implemented candepend on the network topologies and/or protocols implemented, and viceversa. In many embodiments, exemplary communication hardware cancomprise wired communication hardware including, for example, one ormore data buses, such as, for example, universal serial bus(es), one ormore networking cables, such as, for example, coaxial cable(s), opticalfiber cable(s), and/or twisted pair cable(s), any other suitable datacable, etc. Further exemplary communication hardware can comprisewireless communication hardware including, for example, one or moreradio transceivers, one or more infrared transceivers, etc. Additionalexemplary communication hardware can comprise one or more networkingcomponents (e.g., modulator-demodulator components, gateway components,etc.)

Turning ahead in the drawings, FIG. 5 illustrates a flow chart for amethod 500, according to an embodiment. Method 500 is merely exemplaryand is not limited to the embodiments presented herein. Method 500 canbe employed in many different embodiments or examples not specificallydepicted or described herein. In some embodiments, the activities ofmethod 500 can be performed in the order presented. In otherembodiments, the activities of method 500 can be performed in anysuitable order. In still other embodiments, one or more of theactivities of method 500 can be combined or skipped. In manyembodiments, system 300 (FIG. 3 ) can be suitable to perform method 500and/or one or more of the activities of method 500. In these or otherembodiments, one or more of the activities of method 500 can beimplemented as one or more computer instructions configured to run atone or more processing modules and configured to be stored at one ormore non-transitory memory storage modules 412, 414, 422, 424, and/or462 (FIG. 4 ). Such non-transitory memory storage modules can be part ofa computer system such as search system 310 (FIGS. 3 & 4 ),personalization system 320 (FIGS. 3 & 4 ), and/or display system 360(FIGS. 3 & 4 ). The processing module(s) can be similar or identical tothe processing module(s) described above with respect to computer system100 (FIG. 1 ).

In many embodiments, method 500 can be a method of whole pagepersonalization to personalize a web page based on user intent,information from a search query (e.g., search terms) and/or other itemactivity. For example, method 500 can comprise an activity 505 ofmodeling a webpage as a random field, wherein the random field comprisesan undirected graph. In some embodiments, activity 505 can comprise amaximum entropy model. In many embodiments, the undirected graph cancomprise one or more nodes and one or more edges. In many embodiments,each edge of the one or more edges is between two different nodes of theone or more nodes, and each node of the one or more nodes can compriseone or more placements on the webpage and a goodness function of one ormore goodness functions associated with one or more webpage modules. Insome embodiments, the goodness function can comprise a probabilisticmodel learnt from historical session data (e.g., historical datadescribed below). In many embodiments, each edge of the or one moreedges can comprise a compatibility function based at least in part onthe one or more goodness functions of the two different nodes of the oneor more nodes associated with each edge of the one or more edges. Insome embodiments, the compatibility function can comprise aprobabilistic model learnt from historical session data (e.g.,historical data described below). In some embodiments, activity 505 cancomprise using item activity statistics from the user and/or other users(e.g., page views, item clicks, item add-to-carts, and/or itempurchases) in a taxonomy (e.g., Baby, Baby/Nursery,Baby/Nursery/Furniture, Baby/Nursery/Furniture/Cribs). In manyembodiments, each of the item activity statistics can be computed acrossvarious time resolutions (e.g., over a year, 6 months, month, week, dayand/or hour). Such temporal features can provide additional informationto the modeling of the undirected graph (e.g., a purchase of atelevision in the last 30 days is different from buying a consumable inthe last month, as the consumable offers an opportunity for areplenishment themed campaign, while the former can be a cross sellopportunity (e.g., to promote home theatre systems and console tables)).In many embodiments, activity 505 can further comprise modeling thewebpage based at least in part on a user profile (described below) ofthe user and/or other users.

In many embodiments, method 500 can further comprise an activity 510 ofdetermining a probability of the webpage having exceeded a predeterminedthreshold to likely cause a user to take an action (e.g., view a producton the webpage, click on the product on the webpage, add the product toa checkout cart, and/or purchase the product) based at least in part onthe one or more compatibility functions. In some embodiments, activity510 can comprise determining a probability of the webpage havingexceeded a predetermined threshold based at least in part on the one ormore compatibility functions by: activity 515 of determining a firstplacement of the one or more placements of a first webpage module of theone or more webpage modules; and activity 520 determining a secondplacement of the one or more placements of a second webpage module ofthe one or more webpage modules. In many embodiments, the one or morewebpage modules can comprise an advertisement, an advertisement banner,a header message, a point of view, a vertical banner, an item carousel,a search recommendation, or an item recommendation.

In some embodiments, the one or more placements comprise contiguousplacements of the one or more webpage modules on the webpage. In someembodiments, activity 510 further can comprise determining approximately1-100 additional placements of the one or more placements of one or moreadditional webpage modules of the one or more webpage modules. In manyembodiments, the one or more placements comprise contiguous placementsof the one or more webpage modules on the webpage. In some embodiments,the predetermined threshold can be based at least in part on thecompatibility score of each of the one or more edges. In someembodiments, the predetermined threshold comprises a compatibility scoreof at least approximately 0.5 or 50%. In some embodiments, thecompatibility score of each the one or more edges can be based at leastin part on an engagement value, the engagement value comprising at leastone of: a click rate (e.g., a rate at which the user clicks on one ormore items on the webpage), a bounce rate (e.g., a rate at which theuser leaves a webpage to go to a different webpage, or a rate at whichthe user leaves one or more items such as clicking a first item and thenleaving the first item by clicking a second item), an add to cart rate(e.g., a rate at which the user adds an item to cart after viewing theitem), or a purchase rate (e.g., a rate at which the user purchases theitem after viewing it and/or after adding the item to the cart).

In some embodiments, the model of activity 505 can be trained usinghistorical data, (e.g., historical webservice logs) to generate positiveand negative samples. In some embodiments, to improve conversion rate(e.g., viewing an item to purchasing the item), positive labels can beused as a set of all sessions which had a conversion related activity ina relevant category after seeing a personalized webpage (e.g., a userwas exposed to a personalized electronics experience and buys or adds tocart a camera in the same browse session). In many embodiments, amaximum likelihood estimation can be used to estimate the maximumentropy model. In many embodiments, a preferred parameter configurationcan be determined by maximizing a conditional log probability ofobserving the training data (e.g., the historical data)) x, over thetime resolution or time period t:

$\begin{matrix}{{{\arg\max_{w}{\sum\limits_{t}{\log{p\left( {{c_{t}❘x_{i}},w} \right)}}}} = {\arg\max_{w}{\sum\limits_{t}\frac{1}{1 + {\exp\left( {{- w^{\top}}x} \right)}}}}};} & \left( {{Equation}1} \right)\end{matrix}$

where c_(t) is a random variable denoting the desired discrete response,P(c_(t)|x_(t)|w) is the probability of seeing the desired response,given observations x_(t) and the model parameters w, P(c_(t)=1|x_(t)|w)is the probability of conversion given observations x_(t) and modelparameters w, and w denotes the model parameters that can be learnedfrom the historical data. In some embodiments, an array of real valuedweights can indicate the importance of features (in the array) x_(t). Inmany embodiments, in case of conversion, c_(t)=1 would indicate aconversion event, and c_(t)=0 otherwise,

In some embodiments, activity 510 of determining the probability of thewebpage is further based at least in part on a user profile of the userand/or other users. In various embodiments, the user profile of the userand/or other users can comprise demographic information associated withthe related one or more users, likes and dislikes associated with therelated one or more users, and/or shopping, pickup, and deliverypreferences associated with the related one or more users.

In some embodiments, activity 510 further can comprise determining theprobability of the webpage by using the formula in Equation 2:

$\begin{matrix}{{{P({page})} = {\frac{1}{Z}{\prod_{i}{{\varnothing_{i}(x)}{\prod_{i,{j;{{Edge}({i,j})}}}{\varphi_{i,j}\left( {x,y} \right)}}}}}};} & \left( {{Equation}2} \right)\end{matrix}$

wherein: P(page) is the probability of the webpage, Z is a normalizationconstant (e.g., a partition function), i is a first node of the one ormore nodes, j is a second node of the one or more nodes, x is a firstplacement of the one or more placements, Ø_(i)(x) is a first goodnessfunction of the one or more goodness functions of the first node at thefirst placement of the one or more placements, Π_(i)└_(i)(x) is theproduct of the goodness function of the first node, y is a secondplacement of the one or more placements of the second node of the one ormore nodes, φ_(i,j)(x,y) is the compatibility function, andΠ_(i,j;Edge(i,j))φ_(i,j)(x,y) is the product of the compatibility of thefirst placement of the one or more placements and the second placementof the one or more placements.

Turning briefly to FIG. 6 , FIG. 6 illustrates an exemplary webpage 600and/or an undirected graph according to many embodiments. In someembodiments, webpage 600 can comprise two or more placements, oravailable spots, for one or more webpage modules. In some embodiments,for example, from top to bottom and left to right, webpage 600 cancomprise a first placement 605, a second placement 610, a thirdplacement 615, a fourth placement 620, a fifth placement 625, and asixth placement 630. In other embodiments, a webpage (e.g., webpage 600)can comprise 1-100 placements in different configurations. In someembodiments, the one or more placements or nodes of webpage 600 cancomprise associated one or more edges between the one or more placements(e.g., edges 635, 640, 645, 650, and 655).

In some embodiments, first placement 605 can comprise a header messagewebpage module, second placement 610 can comprise a left navigation barwebpage module, third placement 615 can comprise a point of view webpagemodule, fourth placement 620 can comprise an advertisement bannerwebpage module, fifth placement 625 can comprise an item carouselwebpage module, and sixth placement 630 can comprise a vertical carouselwebpage module. In many embodiments, a node of the undirected graph cancomprise a website placement (e.g., first placement 605) and a goodnessfunction of one or more goodness functions associated with the webpagemodule (e.g., header message) of the one or more webpage modules. Inmany embodiments, each edge between two nodes (e.g., edge 635) comprisesa compatibility function based at least in part on the one or moregoodness functions of the two different nodes (e.g., first placement 605and second placement 610) of the one or more nodes associated with eachedge of the one or more edges.

Returning to FIG. 5 , in many embodiments, method 500 can furthercomprise an activity 525 of facilitating display of the webpage based atleast in part on the probability of the webpage. The one or more webpagemodules can be arranged on the webpage pursuant to a ranking of one ormore webpage modules, and the ranking can be based, at least in part, onthe compatibility score of each of the one or more edges and/or thewebpage personalization described herein.

In some embodiments, method 500 can further comprise receiving anaffinity score of the user for each of one or more categories (e.g., aspecific category in an item taxonomy and/or a custom segment), genders,brands, locations, and/or other custom segments (e.g., an allergy, suchas gluten free, and/or preferences, such as organic), the one or morewebpage modules comprising the one or more categories. In someembodiments, the affinity score of the user for each of one or morecategories can be based at least in part on the user profile of the userand/or recent item activity (e.g., one or more search queries by theuser, item clicks by the user, and/or item purchases by the user) of theuser.

In many embodiments, method 500 further can comprise receiving a searchquery from a search from a user. In some embodiments, activity 510(described above) further can comprise determining the probability ofthe webpage is further based at least in part on the search query of theuser. In some embodiments, the search query is received from the userduring a browse session. In some embodiments, the browse session cancomprise a time period spent on a website and/or other third partywebsites. In some embodiments, the time period can be approximately 1second to approximately 1 hour. In some embodiments, the time period canbe the time that the user is logged into a session. In some embodiments,the time period can be from when the user logs into a session to whenthe user closes a browser. In some embodiments, receiving the searchquery from the search by the user can comprise receiving the searchquery during a time window. In some embodiments, the time window cancomprise the browse session time period. In some embodiments, the timewindow can comprise a number of item activities associated with thebrowse session. In various embodiments, the item activity associatedwith the browse session can comprise at least one of a view of an itemof the item set, a click on the item of the item set, an add-to-cart ofthe item of the item set, or a purchase of the item of the item set. Ina number of embodiments, the time window can comprise a number ofactions, subsequent to the search query, associated with item activityassociated with the browse session (e.g., a number of clicks on one ormore items, a number of views of one or more items, a number of itemsadded to the checkout cart, and/or a number of purchases of one or moreitems). In some embodiments the number of subsequent actions cancomprise a combination of a number of item activities. In someembodiments, the number of subsequent actions can comprise approximately1 to 100 item activities.

In some embodiments, method 500 further can comprise an activity ofreceiving one or more previous search queries from a search database,the one or more previous search queries related to the search query. Anadvantage of the activity of receiving one or more previous searchqueries from a search database, the one or more previous search queriesrelated to the search query, can comprise expanding a source ofinformation associated with previous search queries for one or moresearches related to the search query. The source of information cancomprise when an other user searched for a related search query and theitem activity associated to the other user's search for the relatedsearch query. In some embodiments, the user profile of the user and/orother users can be updated based at least in part on the search query ofthe user and/or other users.

In some embodiments of method 500, as an example, assuming 3 webpagemodules comprising 3 item carousels are to be displayed in a webpage tothe user. In many embodiments, the 3 carousels can be selected from aset of category-based item carousels. First, an affinity of the user toall carousels in the set of category-based item carousels can bereceived from a machine learning algorithm. In many embodiments, theaffinity of the user to all carousels in the set of category-based itemcarousels can be received with the following values:

Apparel 0.01

Auto 0.12

Baby 0.60

Electronics 0.03

Food 0.08

Home 0.02

Household 0.01

Pets 0.01

Toys 0.11

VG (Video Games) 0.01

In the first iteration, method 500 can select the carousel with ahighest affinity for the top placement (e.g., first placement 605 (FIG.6 )) which is the “Baby” carousel in this example. For the secondplacement (e.g., second placement 610 (FIG. 6 )), method 500 can find acarousel with a high affinity for the user, and also is compatible withthe first selected carousel in the first placement. The compatibilityscore across categories can be found using a compatibility matrix:

Apparel Auto Baby Electronics Food Home Household Pets Toys VG Apparel —0.1 0.25 0.1 0.05 0.15 0.1 0.1 0.05 0.1 Auto 0.1 — 0.05 0.3 0.1 0.1 0.10.1 0.05 0.1 Baby 0.25 0.05 — 0.05 0.15 0.1 0.05 0.05 0.25 0.05Electronics 0.1 0.3 0.05 — 0.05 0.05 0.05 0.05 0.1 0.25 Food 0.05 0.10.15 0.05 — 0.15 0.2 0.1 0.1 0.1 Home 0.15 0.1 0.1 0.05 0.15 — 0.2 0.150.05 0.05 Household 0.1 0.1 0.05 0.05 0.2 0.2 — 0.2 0.05 0.05 Pets 0.10.1 0.05 0.05 0.1 0.15 0.2 — 0.15 0.1 Toys 0.05 0.05 0.25 0.1 0.1 0.050.05 0.15 — 0.2 VG 0.1 0.1 0.05 0.25 0.1 0.05 0.05 0.1 0.2 —

The carousel with highest affinity among the remaining carousels is“Auto,” however it has a low compatibility score with “Baby,” 0.05. Inmany embodiments, new affinity scores can be determined by multiplyingthe first affinity scores with the corresponding compatibility score andnormalizing to find the new affinity scores:

Apparel 0.0472

Auto 0.1132

Electronics 0.0283

Food 0.2264

Home 0.0377

Household 0.0094

Pets 0.0094

Toys 0.5189

VG (Video Games) 0.0094

After normalizing, “Toys” carousel has the highest score and selected tobe the second carousel to display on the webpage at the secondplacement. Again iterating once more for the remaining carousels to finda carousel for the third placement, the affinity scores can be updatedby considering the compatibility with “Toys”:

Apparel 0.0222

Auto 0.2667

Electronics 0.1333

Food 0.3556

Home 0.0444

Household 0.0222

Pets 0.0667

VG (Video Games) 0.0889

Using the prior affinity and compatibility scores, method 500 cancomprise selecting the “Food” carousel based on the updated affinityscores.

Returning to FIG. 4 , FIG. 4 illustrates a block diagram of a portion ofsystem 300 comprising search system 310, personalization system 320,and/or display system 360, according to the embodiment shown in FIG. 3 .Each of search system 310, personalization system 320, and/or displaysystem 360 is merely exemplary and is not limited to the embodimentspresented herein. Each of search system 310, personalization system 320,and/or display system 360 can be employed in many different embodimentsor examples not specifically depicted or described herein. In someembodiments, certain elements or modules of search system 310,personalization system 320, and/or display system 360 can performvarious procedures, processes, and/or acts. In other embodiments, theprocedures, processes, and/or acts can be performed by other suitableelements or modules.

In many embodiments, search system 310 can comprise non-transitorymemory storage modules 412 and 414, personalization system 320 cancomprise non-transitory memory storage modules 422 and 424, and displaysystem 360 can comprise a non-transitory memory storage module 462.Memory storage module 412 can be referred to as a user module 412, andmemory storage module 414 can be referred to as a browse and searchmodule 414. Memory storage module 422 can be referred to as a modelingmodule 422, and memory storage module 424 can be referred to as aprobability module. Memory storage module 462 can be referred to as animage module 462.

In many embodiments, user module 412 can store computing instructionsconfigured to run on one or more processing modules and perform one ormore acts of methods 500 (FIG. 5 ) (e.g., activity 505).

In some embodiments, browse and search module 414 can store computinginstructions configured to run on one or more processing modules andperform one or more acts of methods 500 (FIG. 5 ) (e.g., activity 505).

In many embodiments, modeling module 422 can store computinginstructions configured to run on one or more processing modules andperform one or more acts of methods 500 (FIG. 5 ) (e.g., activity 505).In several embodiments, modeling module 422 also can at least partiallyperform activity 710 (FIG. 7 , described below) of modeling a webpage asa random field, activity 720 (FIG. 7 , described below) of identifying acyclic dependency in the undirected graph wherein the cyclic dependencyinvolves at least three of the nodes, activity 740 (FIG. 7 , describedbelow) of breaking one or more of the edges of the undirected graph thatconnects the at least three of the nodes in the cyclic dependency,activity 730 (FIG. 7 , described below) of selecting a configuration ofa pair of nodes when the configuration converges before a number of theiterations conducted for the each pair of nodes exceeds a predeterminednumber of iterations, activity 735 (FIG. 7 , described below) ofselecting a configuration of a latest one of the iterations when theiterations do not result in convergence before a number of theiterations conducted for the each pair of nodes exceeds a predeterminednumber of iterations, activity 745 (FIG. 7 , described below) ofdetecting a dead lock configuration between a first zone of a first nodeadjacent to a second zone of a second node in the undirected graph,activity 755 (FIG. 7 , described below) of retrieving one or morehistorical dead lock configurations from a memory, activity 760 (FIG. 7, described below) of breaking the dead lock configuration by resettinga probability node of the dead lock configuration based on the one ormore historical dead lock configurations, activity 765 (FIG. 7 ,described below) of initiating a random restart of an initial nodeselection of one or more historical dead lock configurations, activity770 (FIG. 7 , described below) of exploring the first zone of the firstnode in the undirected graph for other zones of the first node, and/oractivity 775 (FIG. 7 , described below) of breaking the dead lockconfiguration by exploiting one or more of the other zones of the firstnode.

In many embodiments, probability module 424 can store computinginstructions configured to run on one or more processing modules andperform one or more acts of methods 500 (FIG. 5 ) (e.g., activity 510,activity 515, and/or activity 520). In several embodiments, modelingmodule 424 also can at least partially perform activity 780 (FIG. 7 ,described below) of determining a probability of the webpage havingexceeded a predetermined threshold based on compatibility functions ofthe edges, as updated.

In some embodiments, image module 462 can store computing instructionsconfigured to run on one or more processing modules and perform one ormore acts of methods 500 (FIG. 5 ) (e.g., activity 525). In variousembodiments, modeling module 462 also can at least partially performactivity 790 (FIG. 7 , described below) of sending instructions todisplay the webpage based at least in part on the probability of thewebpage having exceeded the predetermined threshold.

Turning ahead in the drawings, FIGS. 7A-7B illustrate a flowchart for amethod 700, according to another embodiment. In some embodiments, method700 can be a method of whole page personalization to personalize a webpage associated with a cyclic dependency between one or more nodes in anundirected graph. In some embodiments, the method of whole pagepersonalization to personalize the web page can be based on user intent,information from a search query (e.g., search terms) and/or other itemactivity, similar or identical to the procedures, processes, and/oractivities of method 500 (FIG. 5 ). Method 700 is merely exemplary andis not limited to the embodiments presented herein. Method 700 can beemployed in many different embodiments and/or examples not specificallydepicted or described herein. In some embodiments, the procedures, theprocesses, and/or the activities of method 700 can be performed in theorder presented. In other embodiments, the procedures, the processes,and/or the activities of method 700 can be performed in any suitableorder. In still other embodiments one or more of the procedures, theprocesses, and/or the activities of method 700 can be combined orskipped. In several embodiments, system 300 (FIG. 3 ) can be suitable toperform method 700 and/or one or more of the activities of method 700.In various embodiments, portions of the procedures, processes, and/oractivities of method 500 can be performed interchangeably as part ofmethod 700 and vice versa.

In these or other embodiments, one or more of the activities of method700 can be implemented as one or more computing instructions configuredto run at one or more processors and configured to be stored at one ormore non-transitory computer-readable media. Such non-transitorycomputer-readable media can be part of a computer system such as searchsystem 310, personalization system 320, display system 360, and/ornetwork 330. The processor(s) can be similar or identical to theprocessor(s) described above with respect to computer system 100 (FIG. 1).

Referring to FIG. 7 , method 700 can include an activity 710 of modelinga webpage as a random field. In some embodiments, the random field caninclude an undirected graph comprising nodes and edges, as describedbelow in connection with webpage as a random field with non-cyclicdependencies 800 (FIG. 8 , described below) and/or webpage as a randomfield with cyclic dependencies 900 (FIG. 9 , described below). Such anundirected graph comprising nodes and edges can be similar or identicalundirected graph created by activity 505 (FIG. 5 ). In many embodiments,selecting the zones or placements that are compatible with each othercan be based at least in part on a user profile of the user and/or otherusers, as described above in connection with activity 505 (FIG. 5 ,above). In several embodiments, the webpage as a random field caninclude a first order Markov graph and/or a graph in a planar layoutwith one or more zones appearing one after another in the undirectedgraph. In various embodiments, the terms zones and/or placements can beused interchangeably, where the zones and/or placements can beassociated with each of the nodes in the undirected graph of the webpage. In some embodiments, a first order Markov graph can form a chainof connected nodes where each node on the chain can be dependent on anadjacent node or a neighbor node. In many embodiments, modeling thewebpage without dependency cycles can form an undirected graph withnon-cyclic dependencies. In various embodiments, determining one or morecompatible assets for one or more zones for each node can based on a setof available assets for a zone and/or a pool of available assets of auser, as described above in greater detail in connection with activities505 and 510 (FIG. 5 ).

In many embodiments, the modeling the webpage in activity 505 (FIG. 5 )and/or activity 710 can be determined using Equation 1 to estimate amaximum likelihood estimation that can determine the maximum entropymodel. In various embodiments, modeling a webpage for a user also caninclude a selection process that can be used to select one or moreassets for each zone corresponding to each node connected in theundirected graph. In some embodiments, such a selection process can beimplemented by using a selection algorithm, as described below.

a ₁=argmax_(r∈A) g(r)

where g is a goodness of an asset, C is a compatibility function, A is aset of all available pool of assets, an a_(i) asset that is selected fora zone_(i), where A(a₁, . . . , a_(i))=a set of available pool of assetscompatible with a_(i) after assets a₁ to a_(i) are used, and Z=a set ofzones 1 to n.

-   -   For each zone i in 2 to n: max score=0.

For r in A(a₁,.., a_(i−1)) : {  asset_score = f(g(r), C(r, a_(i−1)))  if(asset_score > max_score): {   a_(i) = r   max_score = asset_score  } }

In various embodiments, this selection algorithm can be based on a userprofile, user preferences, user activities, and/or another suitable datapoint for the user. In many embodiments, the user profile can include apool of assets of the user. The user profile can be similar or identicalto the user profiles in activity 510 (FIG. 5 ).

In many embodiments, once the asset is selected for a zone, determininga compatible asset for an adjacent zone in the undirected graph caninclude analyzing a set of available pool of assets. The set ofavailable pool assets can be gathered from a recent user activity, acurrent search query received by the user, and/or another suitable assetselected for a zone in the undirected graph for the user. Such aselection process of each zone that is compatible for an adjacent zonecan be used for both modeling an undirected graph with non-cyclicdependencies (e.g., a planar graph) or cyclic dependencies (e.g., anon-planar graph), as described in connection with activity 505 (FIG. 5, above).

Turning ahead in the drawings, FIG. 8 illustrates an exemplary webpageas a random field with non-cyclic dependencies 800. Webpage as a randomfield with non-cyclic dependencies 800 is merely exemplary. In variousembodiments, webpage as a random field with non-cyclic dependencies 800can include an undirected graph representing an interactive webpage or asoftware application display. In a number of embodiments, webpage as arandom field with non-cyclic dependencies 800 can include a planar graph810 and a planar diagram 820. Webpage as a random field with non-cyclicdependencies 800 can be similar to webpage 600 (FIG. 6 ).

In several embodiments, planar graph 810 can include one or more zonesand/or placements for each node in one or more nodes displayed in aplanar layout for a webpage. Planar graph 810 illustrates an example ofa first order Markov graph format with non-cyclic dependencies amongeach node. In many embodiments, the one or more zones in the one or morenodes can be connected in a chain or a sequential order where every nodeis dependent on an adjacent node and/or a neighbor node illustrating agraph with non-cyclic dependencies. In some embodiments, selecting theone or more zones from a top node down to a bottom node can includeselecting and/or inferring each zone is compatible with a neighbor node.Selecting the one or more zones can be similar or identical to activity505 (FIG. 5 ), activity 510 (FIG. 5 ), and/or activity 710 (FIG. 7 ).For example, a planar graph with non-cyclical dependencies with 4 zonescan be connected by one or more edges from top zone 1 to bottom zone n.The relationship showing the connectivity of each zone in planar graph810 can include: zone 1 dependent on zone 2, zone 2 dependent on zone 1,zone 2 dependent on zone 3, zone 3 dependent on zone 2, zone 3 dependenton zone 4, and zone 4 dependent on zone 3. In another example, a planargraph can include another suitable number of zones, n, where each zoneof the n number of zones can depend on, infer from, or be compatiblewith a neighbor zone in an order from top to bottom.

Planar diagram 820 illustrates an exemplary type of zone and/or type ofplacement displayed in a webpage. Planar diagram 820 can include 4diagram boxes with 4 exemplary types of zones describing each node,where each node can include one or more different zones associated witheach node. For example, zone 1 in planar graph 810 can include anelectronics category in diagram box 1 corresponding to planar diagram820, and so on for diagram boxes 2 to 4. In following with the planargraph display format, diagram box 1 for the electronics category can becompatible with a zone in diagram box 2 for a home category, diagram box2 for the home category can be compatible with a zone in diagram box 3for a toys category, and diagram box 3 can be compatible with a zonefrom diagram box 4 a video games category.

Returning to FIG. 7 , in some embodiments, method 700 also can includean activity 720 of identifying a cyclic dependency in the undirectedgraph. In many embodiments, the cyclic dependency involves at leastthree of the nodes. In some embodiments, selecting assets from more thanone zone can include an incompatible asset of a neighboring zone in theundirected graph. In various embodiments, resolving a cyclic dependencyidentified can allow activity 505 (FIG. 5 ) and activity 510 (FIG. 5 )to be implemented.

In several embodiments, the cyclic dependency in the undirected graph ofactivity 720 optionally can include a set of nodes that can influencemore than one of the at least three of the nodes in the undirectedgraph. In many embodiments, a first node can influence both a secondnode and a third node, and wherein the third node influences both thefirst node and the second node, as described below in connection withnon-planar graph 910 (FIG. 9 ) and non-planar diagram 920 (FIG. 9 ). Forexample, a cyclic dependency can occur when zone 1 can influence aselection from either zone 2 or a selection from zone 3 and vice versafor both zone 2 and zone 3. In such a configuration, a webpage caninclude a compatible asset from zone 1 to zone 2 and an incompatibleasset from zone 2 to zone 3 since each zone can depend on more than oneother zone in the graph.

Turning ahead in the drawings, FIG. 9 illustrates an exemplary webpageas a random field with cyclic dependencies 900. Webpage as a randomfield with cyclic dependencies 900 is merely exemplary. In variousembodiments, webpage as a random field with cyclic dependencies 900 caninclude an undirected graph representing an interactive webpage or asoftware application display. In a number of embodiments, webpage as arandom field with cyclic dependencies 900 can include a non-planar graph910 and a non-planar diagram 920.

In several embodiments, non-planar graph 910 can be a planar layout fora webpage, such as a first order Markov graph, similar to planar graph810 (FIG. 800 ) and/or planar diagram 820 (FIG. 8 ). Non-planar graph910 illustrates a configuration for a graph with cyclic dependencies.Cyclic dependency can occur whether or not the zones that are dependenton more than one other zone are adjacent or neighbor zones in a graph.For example, a graph with cyclic dependencies can have 4 zonesassociated with 4 nodes, where zone 1 can be connected with one or moreedges to zone 2 and to zone 3, and where zone 2 also can be connected tozone 3, and where zone 3 can be connected to zone 4. In this example,zones 1, 2, and 3 can form a complex graph format with cyclicdependencies.

Non-planar diagram 920 illustrates an exemplary type of zone and/or typeof placement of each of 4 zones displayed in a webpage. Non-planardiagram 920 can include 4 diagram boxes with 4 exemplary zones for morethan one node, where each node can include one or more different zonesassociated with each node. For example, zone 1 in non-planar graph 910can include an electronics category in diagram box 1 of non-planardiagram 920, and so on for non-planar diagram boxes 2 to 4. In followingwith the non-planar diagram graph display format, diagram box 1 for theelectronics category can be compatible with a zone in diagram box 2 fora home category and a zone in diagram box 3 for a toys category. Diagrambox 2 for the home category can be compatible with zone 3 and zone 1.Diagram box 3 can be compatible with diagram box zone 1 and diagram boxfor zone 4 for a video games category.

Returning to FIG. 7 , upon identifying the cyclic dependency in theundirected graph, method 700 also optionally can include an activity 725of processing one or more inferences over the cyclic dependency for eachof the at least three nodes. In several embodiments, activity 725 ofprocessing the one or more inferences over the cyclic dependency can beimplemented using a selection process to select one or more assets foreach zone corresponding to each node connected in the undirected graph,such a selection process can be similar or identical to modeling awebpage using the selection process in activity 505 (FIG. 5 ) and/oractivity 710 (FIG. 7 ). In various embodiments, the terms inferencesand/or dependencies can be used interchangeably, where the inferencesand/or dependencies can be associated with the selection process toselect one or more assets for each zone corresponding to each nodeconnected in the undirected graph.

In many embodiments method 700, additionally optionally can include anactivity 727 of conducting iterations for each pair of nodes of the atleast three nodes. For example, each node in the cyclic dependency canbe set to be dependent on a previous node for a single iteration of theprocessing step. As an example, zone 1 can be set to zone 2, zone 2 canbe set to zone 3, and zone 3 can be set to zone 1 to form the singleiteration to be processed. After each iteration has been processed,activity 727 can include repeating the cycle for multiple iterations toprocess the one or more inferences over the cyclic dependency.

In some embodiments, conducting multiple iterations for each pair ofnodes can be based on using the following algorithm:

a _(k) ^(i)=argmax f(g(r),C(r,a _(k) ^(z))) where r∈A

where U: for K iterations and Y: for each zone i in Z,If for all i in Z (a_(k) ^(i)==a_(k−1) ^(i)) break from U,where a_(k) ^(i) refers to an asset that is selected for zone_(i) in kthiteration;z refers to a zone that zone_(i) is dependent upon;g refers to a goodness of asset;C refers to a compatibility function;Z indicates a set of zones with a cyclic dependency; andA indicates a set of available pool of assets.

In some embodiments, method 700 further optionally can include anactivity 730 of selecting a configuration of a pair of nodes when theconfiguration converges before a number of the iterations conducted forthe each pair of nodes exceeds a predetermined number of iterations.

In other embodiments, instead of activity 730, method 700 additionallyoptionally can include an activity 735 of selecting a configuration of alatest one of the iterations when the iterations do not result inconvergence before a number of the iterations conducted for the eachpair of nodes exceeds a predetermined number of iterations.

In a number of embodiments, method 700 further can include an activity740 of breaking one or more of the edges of the undirected graph thatconnects the at least three of the nodes in the cyclic dependency. Whenthe one or more edges are broken, those edges are removed from thegraph, such that such one or more edges are no longer part of the edgesof the graph.

In some embodiments, activity 740 optionally can include breaking theone or more edges of the undirected graph connecting a pair of nodes ofthe at least three of the nodes representing content located below afold of the webpage. In a number of embodiments, one or more assets of azone loaded on a webpage that is visible to users can include zoneslocated above the fold (ATF). In many embodiments, one or more assets ofa zone loaded onto a webpage not visible to a user can include zonesbelow the fold (BTF). In some embodiments, one or more assets of a zoneBTF can be viewed by scrolling down the webpage. In many embodiments,breaking the one or more edges of the undirected graph BTF can removecycles of dependencies in a cyclic dependency configuration by reducinga number of zones that are dependent upon each other. In variousembodiments, breaking the one or more edges can include a cyclicdependency configuration between a zone ATF and a zone BTF.

In many embodiments, whenever a cyclic dependency between a zone AFT anda zone BTF is observed, breaking the edges between the zone AFTconnected to the zone BTF can resolve the cyclic dependencyconfiguration. In some embodiments, breaking the edges below the foldcan include assumptions that a zone ATF and a zone BTF can beindependent of each other in order to reduce a number of zones that canhave cyclic dependency. In several embodiments, reducing the cyclicdependency can allow the system to process one or more inferences overthe cyclic dependency and reach a convergence resolving the cyclicdependency, processing one or more inferences over the cyclic dependencycan be similar or identical to conducting iterations of activities 725,727, and 730. In the example, zone 1 for electronics is dependent onzone 2 for home where both zones are dependent on each other and viewedATF. Zone 1 for electronics is also dependent on zone 4 for video gameswhere zone 1 is ATF and zone 4 is BTF. In this configuration, breakingthe one or more edges between zone 1 and zone 4 can reduce the number ofdependencies on zone 1 resolving the cyclic dependency configuration.

Turning ahead in the drawings, FIG. 10 illustrates an exemplary webpageas a random field with non-cyclic dependencies 1000. Webpage as a randomfield with non-cyclic dependencies 1000 is merely exemplary. In variousembodiments, webpage as a random field with non-cyclic dependencies 1000can include a diagram representing an interactive webpage or a softwareapplication display. In a number of embodiments, webpage as a randomfield with non-cyclic dependencies 1000 can include a non-planar diagram1010.

Non-planar diagram 1010 illustrates one or more zones of one or morenodes displayed ATF (e.g., zones 1, 2, and 3) and a zone of a nodedisplayed BTF (e.g., zone 4). Non-planar diagram 1010 illustrates agraph with a cyclic dependency between a zone ATF and a zone BTF. Insuch a configuration, breaking one or more edges between a zone ATF anda zone BTF can resolve the cyclic dependency between the zones allowinga graph to be displayed with non-cyclic dependencies between each zoneand each neighbor zone.

Returning to the drawings in FIG. 7 , in many embodiments, method 700also optionally can include an activity 745 of detecting a dead lockconfiguration between a first zone of a first node adjacent to a secondzone of a second node in the undirected graph. In several embodiments,the first zone and the second zone can be incompatible zones. In someembodiments, activity 745 of detecting a dead lock configuration can bedetermined based on a set of variables for an algorithm, such as, A=aset of all available pool of assets, A(a₁, . . . , a_(i))=a set ofavailable pool of assets compatible with a_(i) after assets a₁ to a_(i)are used, and Z=set of zones 1 to n.

In several embodiments, detecting the dead lock configuration can bedetermined by using the following algorithm:

 a₁ = ar gmax_(r ∈ A) g(r) where for each zone i in 2 to n: max_score =0 and  for r in A(a₁,.., a_(i−1)) : {   asset_score = f(g(r), C(r,a_(i−1)))   if (asset_score > max_score): {    a_(i) = r    max_score =asset_score   }  }

In many embodiments, set A(a₁, . . . , a_(i−1)) will become empty whenthere is no asset available which is compatible with a_(i−1) which cancause a dead lock configuration. Such a dead lock configuration canoccur when the there is no current selection of a compatible asset(s)for each of the remaining zone(s) in the undirected graph.

In some embodiments, the selection process to select assets for everyposition on a webpage can be conditioned upon each previous assignment.In many embodiments, a selection process can include two competingconstraints (e.g., incompatible zones or placements), such as: agoodness function of an asset for a current position on the webpage anda compatibility score of each of one or more edges of an asset with atleast one neighbor asset. For example, based on an assignment to a zonek, if none of the available assets for a selection for a zone k+1 iscompatible with zone k, the selection for zone k+1 cannot be possible.As another example, based on the previous assignments to a webpage for auser, a category for “baby food” can be compatible for 2 categories froman available pool of assets for the user, such as: “electronics” and“home furniture.” If zone k is assigned a banner about “baby food” andthe only assets available for zone k+1 include advertisements for “motoroil” or “weed killers,” a selection based on the available pool ofassets for zone k+1 is incompatible, thus the inference from zone k tozone k+1 fails.

In a number of embodiments, upon detection of the dead lockconfiguration, method 700 additionally optionally can include anactivity 750 of initiating a random restart of an initial node selectionbased on at least a weighted random sampling of the nodes in theundirected graph. In many embodiments, the random restart approach caninclude selected at random or randomizing an initial node selection. Insome embodiments each of the nodes in the undirected graph can be givena weighted score using a weighted random sampling approach. In variousembodiments, an advantage of using the weighted random sampling approachcan include increasing the likelihood that the nodes with highergoodness functions that other goodness functions across other nodes canbe selected when using the random restart approach to break the deadlock situation.

In several embodiments, resolving the dead lock can include a randomrestart approach, based on the following random restart algorithm:

 a₁ = ar gmax_(r ∈ A) g(r) where, i = 2; Do {  max_score = 0  For r inA(a₁,.., a_(i−1)) : {    asset_score = f(g(r), C(r, a_(i−1)))    if(asset_score > max_score): {     a_(i) = r     max_score = asset_score   }  }  if A(a₁,.., a_(i)) is empty { //detected deadlock   restartZone = getRandom(i−1, 1);    if(restartZone == 1) {    a_(restartZone) = weighted_random_sampled_asset (g(r))    } else {    a_(restartZone) = weighted_random_sampled_asset (f(g(r), C(r,a_(i−1))))    }    i=restartZone + 1  } else {    i++;   } } While (i <=n).

In some embodiments, each zone can include multiple assets of which onlyone asset, as selected, can be displayed for each node. In severalembodiments, the random restart approach can include a random samplingstep by first randomly selecting a zone (e.g., a restart zone) fromamong the zones that created the dead lock configuration to revert backor restart the selection process over using other assets from the zone.In many embodiments, starting from the restart zone (e.g., randomlyselected zone), the random restart algorithm can select an asset with ahigher goodness function and a higher compatibility score of each of theone or more edges over by using other assets in the node. In severalembodiments, the system can implement and/or run a new iteration of theselection process using the new asset to resolve the dead lockconfiguration. In many embodiments, in the event the new asset createsanother dead lock configuration, the random restart approach cancontinue the process with other nodes in the undirected graph. Such aninitial selection process of zones can be similar or identical to theinitial selection procedures, processes, and/or activities used to modelthe webpage page as a random field in activity 505 (FIG. 5 ) and/oractivity 510 (FIG. 5 ).

In some embodiments, after detection of the dead lock configuration,method 700 also optionally can include an activity 755 of retrieving oneor more historical dead lock configurations from a memory.

In many embodiments, after detection of the dead lock configuration,method 700 additionally optionally can include an activity 760 ofbreaking the dead lock configuration by resetting a probability node ofthe dead lock configuration based on the one or more historical deadlock configurations.

In various embodiments, after detection of the dead lock configuration,method 700 further optionally can include an activity 765 of initiatinga random restart of an initial node selection of one or more historicaldead lock configurations. In some embodiments, initiating the randomrestart with memory (e.g., historical dead lock configurations) can besimilar to activity 505 (FIG. 5 ) and/or activity 510 (FIG. 5 ). Invarious embodiments, the random restart with memory can be similar toactivity 750, with the exception that a random restart with memoryalgorithm described below can keep track of historical asset selectionsthat previously led to historical dead lock configurations so as not toselect a same or a similar asset combination from the historical assetselections.

In many embodiments, resolving the dead lock can include the randomrestart with memory approach:

 a₁ = ar gmax_(r ∈ A) g(r) where i = 2; Do {  max_score = 0  For r inA(a₁,.., a_(i−1)) : {   asset_score = f(g(r), C(r, a_(i−1)))   if(asset_score > max_score): {    a_(i) = r    max_score = asset_score   } }  if A(a₁,.., a_(i)) is empty { //detected deadlock   A(a₁,..,a_(i−1)) = A(a₁,.., a_(i−1)) − a_(i) // Memorize that a_(i) leads todeadlock given (a₁,.., a_(i−1))   restartZone = getRandom(i−1, 1);  if(restartZone == 1) {    a_(restartZone) =weighted_random_sampled_asset (g(r))   } else {    a_(restartZone) =weighted_random_sampled_asset (f(g(r), C(r, a_(i−1))))   }  i=restartZone + 1  } else {   i++;  } } While ( i <= n)

In various embodiments, the random restart with memory algorithm caninclude a random sampling step by first randomly selecting a zone (e.g.,a restart zone) from among the zones that created the dead lockconfiguration to revert back or restart the selection process over usingother assets from the zone. In some embodiments, the set of availablepool assets can be updated by removing and/or flagging the assetcombinations that ended up or generated dead lock configurations. Inmany embodiments, starting from the restart zone (e.g., randomlyselected zone), the random restart with memory algorithm can select anasset with a higher goodness function and a higher compatibility scoreof each of the one or more edges over other assets in the node. Inseveral embodiments, the system can implement and/or run a new iterationof the selection process using the new asset to resolve the dead lockconfiguration. In many embodiments, in the event the new asset createsanother dead lock configuration, the random restart approach cancontinue the process with other nodes in the undirected graph. Such aninitial selection process of zones can be similar or identical to theinitial selection procedures, processes, and/or activities used to modelthe webpage page as a random field in activity 505 (FIG. 5 ) and/oractivity 510 (FIG. 5 ).

In a number of embodiments, after detection of the dead lockconfiguration, method 700 also optionally can include an activity 770 ofexploring the first zone of the first node in the undirected graph forother zones of the first node.

In some embodiments, after detection of the dead lock configuration,method 700 further optionally can include an activity 775 of breakingthe dead lock configuration by exploiting one or more of the other zonesof the first node. In many embodiments, the one or more other zones arecompatible zones between the first node and the second node.

In many embodiments, resolving the dead lock configuration can includean explore and exploit approach, based on using the followingexplore/exploit algorithm:

 a₁ = ar gmax_(r ∈ A) g(r) where exploreExploit=false; i = 2; Do { if(exploreExploit) {   a_(i) = EE_weighted_random_sample(A− {a₁,..,a_(i−1)});  } else {   max_score = 0   For r in A(a₁,.., a_(i−1)) : {   asset_score = f(g(r), C(r, a_(i−1)))    if (asset_score > max_score):{     a_(i) = r     max_score = asset_score    }   }   if A(α₁,..,a_(i)) is empty { //detected deadlock    exploreExploit =true;    }  } }While ( i <= n)

In many embodiments, resolving dead lock configuration can begin byusing the explore/exploit algorithm to select a new asset of the node byselecting a score for user behavior (e.g., the user profile of the userand/or recent item activity of the user) for each asset. In someembodiments, the explore/exploit algorithm can use previous activitiesand/or behavior of one or more users to assign a score for each assetbased a predetermined number of user activities, such user activitiescan include an average click through rate, an average conversion rate,and/or another suitable user activity data point. In variousembodiments, based on explore/exploit scores previously assigned to theassets of each node, the explore/exploit algorithm also can use aweighted random sampling of each asset in the set of available assets torun a predetermined number of iterations for each asset in the set ofavailable assets.

In several embodiments, method 700 also can include activity 780 ofdetermining a probability of the webpage having exceeded a predeterminedthreshold based on compatibility functions of the edges, as updated,which can be similar to activity 510 (FIG. 5 ).

In some embodiments, activity 780 optionally can include compatibilityfunctions that can quantify compatibility of the edges based on one ormore goodness functions of pairs of the nodes, as described inconnection with activities 505 and 510 (FIG. 5 ).

In many embodiments, method 700 additionally can include an activity 790of sending instructions to display the webpage based at least in part onthe probability of the webpage having exceeded the predeterminedthreshold, which can be similar to activity 525 (FIG. 5 ).

In a number of embodiments, the techniques described herein can solve atechnical problem that arises only within the realm of computernetworks, as modeling a webpage personalized for each user to createonline orders do not exist outside the realm of computer networks.Moreover, the techniques described herein can solve a technical problemthat cannot be solved outside the context of computer networks.Specifically, the techniques described herein cannot be used outside thecontext of computer networks, in view of a lack of data, and because themachine learning model cannot be performed without a computer.

In many embodiments, the machine learning model can be pre-trained, butcan also consider both historical and dynamic input from each event withcurrent data in each time slot. In many embodiments, the techniquedescribed herein can allow the machine learning to train itself to learnwith each iteration.

Although systems and methods for whole page personalization with cyclicdependencies have been described above, it will be understood by thoseskilled in the art that various changes may be made without departingfrom the spirit or scope of the disclosure. Accordingly, the disclosureof embodiments is intended to be illustrative of the scope of thedisclosure and is not intended to be limiting. It is intended that thescope of the disclosure shall be limited only to the extent required bythe appended claims. For example, to one of ordinary skill in the art,it will be readily apparent that any element of FIGS. 1-10 may bemodified, and that the foregoing discussion of certain of theseembodiments does not necessarily represent a complete description of allpossible embodiments. For example, one or more of the activities ofFIGS. 5 and 7 may include different activities and/or be performed bymany different modules, in many different orders. As another example,one or more of the procedures, processes, and/or activities of one ofFIGS. 5 and 7 can be performed in another one of FIGS. 5 and 7 . Asanother example, the systems within system 300 in FIG. 3 can beinterchanged or otherwise modified.

Replacement of one or more claimed elements constitutes reconstructionand not repair. Additionally, benefits, other advantages, and solutionsto problems have been described with regard to specific embodiments. Thebenefits, advantages, solutions to problems, and any element or elementsthat may cause any benefit, advantage, or solution to occur or becomemore pronounced, however, are not to be construed as critical, required,or essential features or elements of any or all of the claims, unlesssuch benefits, advantages, solutions, or elements are stated in suchclaim.

Moreover, embodiments and limitations disclosed herein are not dedicatedto the public under the doctrine of dedication if the embodiments and/orlimitations: (1) are not expressly claimed in the claims; and (2) are orare potentially equivalents of express elements and/or limitations inthe claims under the doctrine of equivalents.

What is claimed:
 1. A system comprising: one or more processors; and oneor more non-transitory computer-readable media storing computinginstructions that, when run on the one or more processors, cause the oneor more processors to perform functions comprising: reconfiguring awebpage as an undirected graph; identifying a cyclic dependency in theundirected graph, wherein the cyclic dependency comprises a set of nodesof the undirected graph; iterating processing one or more inferencesover the cyclic dependency for each pair of the nodes of the set of thenodes; breaking one or more of edges of the undirected graph, whereinthe one or more edges couple together the set of the nodes; determining,based at least in part on compatibility functions of the one or moreedges remaining after breaking the one or more of the edges, aprobability of the webpage having exceeded a predetermined threshold tocause a user to take an action; and sending instructions to display thewebpage.
 2. The system of claim 1, wherein the cyclic dependency in theundirected graph further comprises: connecting nodes that influence morethan one of the set of the nodes in the undirected graph, wherein afirst node influences both a second node and a third node, and whereinthe third node influences both the first node and the second node,wherein the set of nodes comprise the first, second, and third nodes,wherein the nodes comprise the set of nodes, and wherein each node ofthe set of the nodes in the cyclic dependency is dependent on arespective previous node for a respective single iteration of the one ormore inferences.
 3. The system of claim 1, wherein iterating processingthe one or more inferences over the cyclic dependency comprises: afteran initial iteration is processed, repeating a new iteration cycle foriterations to process the one or more inferences over the cyclicdependency.
 4. The system of claim 3, wherein the computinginstructions, when executed on the one or more processors, further causethe one or more processors to perform a function comprising: selecting aconfiguration of a pair of the nodes when the configuration of the pairof the nodes converges before a respective number of the iterationsconducted for each pair of the nodes exceeds a predetermined number ofthe iterations.
 5. The system of claim 3, wherein the computinginstructions, when executed on the one or more processors, further causethe one or more processors to perform a function comprising: selecting aconfiguration of a latest one of the iterations when the iterations donot result in convergence before a respective number of the iterationsconducted for each pair of the nodes exceeds a predetermined number ofthe iterations.
 6. The system of claim 1, wherein breaking the one ormore of the edges of the undirected graph comprises: connecting a pairof the nodes of the set of the nodes representing content located belowa fold of the webpage.
 7. The system of claim 1, wherein the computinginstructions, when executed on the one or more processors, further causethe one or more processors to perform a function comprising: detecting adead lock configuration between a first zone of a first node adjacent toa second zone of a second node in the undirected graph, wherein thefirst zone and the second zone are incompatible zones, and wherein theset of nodes comprise the first and second nodes.
 8. The system of claim7, wherein the computing instructions, when executed on the one or moreprocessors, further cause the one or more processors to performfunctions comprising, upon detection of the dead lock configuration:initiating a random restart of an initial node selection based on atleast a weighted random sampling of the nodes in the undirected graph;retrieving one or more historical dead lock configurations from amemory; breaking the dead lock configuration by resetting a probabilitynode of the dead lock configuration based on the one or more historicaldead lock configurations; and initiating a random restart of an initialnode selection of the one or more historical dead lock configurations.9. The system of claim 7, wherein the computing instructions, when runon the one or more processors, further cause the one or more processorsto perform functions comprising, upon detection of the dead lockconfiguration: exploring the first zone of the first node in theundirected graph for other zones of the first node; and breaking thedead lock configuration by exploiting one or more of the other zones ofthe first node, wherein the one or more other zones are compatible zonesbetween the first node and the second node.
 10. The system of claim 1,wherein the compatibility functions quantify compatibility of the one ormore edges based on one or more goodness functions of pairs of thenodes.
 11. A method being implemented via execution of computinginstructions configured to run on one or more processors and stored onone or more non-transitory computer-readable media, the methodcomprising: reconfiguring a webpage as an undirected graph; identifyinga cyclic dependency in the undirected graph, wherein the cyclicdependency comprises a set of nodes of the undirected graph; iteratingprocessing one or more inferences over the cyclic dependency for eachpair of the nodes of the set of the nodes; breaking one or more of edgesof the undirected graph, wherein the one or more edges couple togetherthe set of the nodes; determining, based at least in part oncompatibility functions of the one or more edges remaining afterbreaking the one or more of the edges, a probability of the webpagehaving exceeded a predetermined threshold to cause a user to take anaction; and sending instructions to display the webpage.
 12. The methodof claim 11, wherein the cyclic dependency in the undirected graphfurther comprises: connecting nodes that influence more than one of theset of the nodes in the undirected graph, wherein a first nodeinfluences both a second node and a third node, and wherein the thirdnode influences both the first node and the second node, wherein the setof nodes comprise the first, second, and third nodes, wherein the nodescomprise the set of nodes, and wherein each node of the set of the nodesin the cyclic dependency is dependent on a respective previous node fora respective single iteration of the one or more inferences.
 13. Themethod of claim 11, wherein iterating processing the one or moreinferences over the cyclic dependency comprises: after an initialiteration is processed, repeating a new iteration cycle for iterationsto process the one or more inferences over the cyclic dependency. 14.The method of claim 13 further comprising: selecting a configuration ofa pair of the nodes when the configuration of the pair of the nodesconverges before a respective number of the iterations conducted foreach pair of the nodes exceeds a predetermined number of the iterations.15. The method of claim 13 further comprising: selecting a configurationof a latest one of the iterations when the iterations do not result inconvergence before a respective number of the iterations conducted foreach pair of the nodes exceeds a predetermined number of the iterations.16. The method of claim 11, wherein breaking the one or more of theedges of the undirected graph comprises: connecting a pair of the nodesof the set of the nodes representing content located below a fold of thewebpage.
 17. The method of claim 11, wherein further comprising:detecting a dead lock configuration between a first zone of a first nodeadjacent to a second zone of a second node in the undirected graph,wherein the first zone and the second zone are incompatible zones, andwherein the set of nodes comprise the first and second nodes.
 18. Themethod of claim 17 further comprising, upon detection of the dead lockconfiguration: initiating a random restart of an initial node selectionbased on at least a weighted random sampling of the nodes in theundirected graph; retrieving one or more historical dead lockconfigurations from a memory; breaking the dead lock configuration byresetting a probability node of the dead lock configuration based on theone or more historical dead lock configurations; and initiating a randomrestart of an initial node selection of the one or more historical deadlock configurations.
 19. The method of claim 17 further comprising, upondetection of the dead lock configuration: exploring the first zone ofthe first node in the undirected graph for other zones of the firstnode; and breaking the dead lock configuration by exploiting one or moreof the other zones of the first node, wherein the one or more otherzones are compatible zones between the first node and the second node.20. The method of claim 11, wherein the compatibility functions quantifycompatibility of the one or more edges based on one or more goodnessfunctions of pairs of the nodes.